What should investors do about these head-spinning changes in the market’s direction? In a word: nothing. Ignoring the shifts in the markets was my main advice for long-term investors throughout the winter, when stocks were cratering. And I’m sticking with it. As it turns out, doing nothing was the second-best advice anyone could have given you for investing in the overall stock market this year.
The very best advice would have required a crystal ball: You should have sold precisely on Jan. 3, when the S&P 500 stock index was at its peak, and bought on June 16, when it hit bottom. (Then, quite possibly, you should have sold again on Aug. 16, before the market turned rocky. The verdict is still out on that one.)
If you made all of those moves, more power to you. Please get in touch and explain how you managed that perfect trick of market timing, and how you’re going to pull it off the next time. For most of us, buying and selling at the right moment isn’t going to happen regularly enough to beat the market….Continue reading…
The efficient market hypothesis posits that stock prices are a function of information and rational expectations, and that newly revealed information about a company’s prospects is almost immediately reflected in the current stock price. This would imply that all publicly known information about a company, which obviously includes its price history, would already be reflected in the current price of the stock.
Accordingly, changes in the stock price reflect release of new information, changes in the market generally, or random movements around the value that reflects the existing information set. Burton Malkiel, in his influential 1973 work A Random Walk Down Wall Street, claimed that stock prices could therefore not be accurately predicted by looking at price history. As a result, Malkiel argued, stock prices are best described by a statistical process called a “random walk” meaning each day’s deviations from the central value are random and unpredictable.
This led Malkiel to conclude that paying financial services persons to predict the market actually hurt, rather than helped, net portfolio return. A number of empirical tests support the notion that the theory applies generally, as most portfolios managed by professional stock predictors do not outperform the market average return after accounting for the managers’ fees.
Fundamental analysts are concerned with the company that underlies the stock itself. They evaluate a company’s past performance as well as the credibility of its accounts. Many performance ratios are created that aid the fundamental analyst with assessing the validity of a stock, such as the P/E ratio. Warren Buffett is perhaps the most famous of all fundamental analysts. He uses the overall market capitalization-to-GDP ratio to indicate the relative value of the stock market in general, hence this ratio has become known as the “Buffett indicator“.
What fundamental analysis in the stock market is trying to achieve, is finding out the true value of a stock, which then can be compared with the value it is being traded with on stock markets and therefore finding out whether the stock on the market is undervalued or not. Finding out the true value can be done by various methods with basically the same principle. The principle is that a company is worth all of its future profits added together. These future profits also have to be discounted to their present value.
This principle goes along well with the theory that a business is all about profits and nothing else. Contrary to technical analysis, fundamental analysis is thought of more as a long-term strategy. Fundamental analysis is built on the belief that human society needs capital to make progress and if a company operates well, it should be rewarded with additional capital and result in a surge in stock price.
Fundamental analysis is widely used by fund managers as it is the most reasonable, objective and made from publicly available information like financial statement analysis. Another meaning of fundamental analysis is beyond bottom-up company analysis, it refers to top-down analysis from first analyzing the global economy, followed by country analysis and then sector analysis, and finally the company level analysis.
Technical analysts or chartists are usually less concerned with any of a company’s fundamentals. They seek to determine possibilities of future stock price movement largely based on trends of the past price (a form of time series analysis). Numerous patterns are employed such as the head and shoulders or cup and saucer. Alongside the patterns, techniques are used such as the exponential moving average (EMA), oscillators, support and resistance levels or momentum and volume indicators.
Candle stick patterns, believed to have been first developed by Japanese rice merchants, are nowadays widely used by technical analysts. Technical analysis is rather used for short-term strategies, than the long-term ones. And therefore, it is far more prevalent in commodities and forex markets where traders focus on short-term price movements. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends and lastly that history (of prices) tends to repeat itself which is mainly because of the market psychology.
Tobias Preis et al. introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends.Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports, suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets. Out of these terms, three were significant at the 5% level (|z| > 1.96). The best term in the negative direction was “debt”, followed by “color”.
In a study published in Scientific Reports in 2013, Helen Susannah Moat, Tobias Preis and colleagues demonstrated a link between changes in the number of views of English Wikipedia articles relating to financial topics and subsequent large stock market moves. The use of Text Mining together with Machine Learning algorithms received more attention in the last years, with the use of textual content from Internet as input to predict price changes in Stocks and other financial markets.
The collective mood of Twitter messages has been linked to stock market performance. The study, however, has been criticized for its methodology. The activity in stock message boards has been mined in order to predict asset returns.The enterprise headlines from Yahoo! Finance and Google Finance were used as news feeding in a Text mining process, to forecast the Stocks price movements from Dow Jones Industrial Average.
Aspect structuring, also referred to as Jacaruso Aspect Structuring (JAS) is a trend forecasting method which has been shown to be valid for anticipating trend changes on various stock market and geopolitical time series datasets. The method addresses the challenge that arises with high dimensional data in which exogenous variables are too numerous or immeasurable to be accounted for and used to make a forecast.
The method identifies the single variable of primary influence on the time series, or “primary factor”, and observes trend changes that occur during times of decreased significance in the said primary variable. Presumably, trend changes in these instances are instead due to so-called “background factors”. Although this method cannot elucidate the multivariate nature of background factors, it can gauge the effects they have on the time-series at a given point in time even without measuring them. This observation can be used to make a forecast.
- “Buffett Indicator: Where Are We with Market Valuations?”.
- “Market Cap to GDP: An Updated Look at the Buffett Valuation Indicator”.
- “Warren Buffett On The Stock Market What’s in the future for investors–another roaring bull market or more upset stomach? Amazingly, the answer may come down to three simple factors.
- “Counting Google searches predicts market movements”.
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Trouble With Your Investment Portfolio? Google It!”.
- Counting Google searches predicts market movements”.
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- Hamish McRae: Need a valuable handle on investor sentiment? Google it”.
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- “Wikipedia’s crystal ball”.
- “Twitter mood predicts the stock market
- Doctoral Thesis: Stock Price Change Prediction Using News Text Mining.
- “A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables”.
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